Dr Shakes Chandra
Dr

Shakes Chandra

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+61 7 336 58359

Background

Shakes an imaging expert that leads a strong deep learning, artificial intelligence (AI) focused research team interested in medical image analysis and signal/image processing applied to many areas of science and medicine. He received his Ph.D in Theoretical Physics from Monash University, Melbourne and has been involved in applying machine learning in medical imaging for over a decade.

Shakes’ past work has involved developing shape model-based algorithms for knee, hip and shoulder joint segmentation that is being developed and deployed as a product on the Siemens syngo.via platform. More recent work involves deep learning based algorithms for semantic segmentation and manifold learning of imaging data. Broadly, he is interested in understanding and developing the mathematical basis of imaging, image analysis algorithms and physical systems. He has developed algorithms that utilise exotic mathematical structures such as fractals, turbulence, group theoretic concepts and number theory in the image processing approaches that he has developed.

He is currently a Senior Lecturer and leads a team of 20+ researchers working image analysis and AI research across healthcare and medicine. He currently teaches the computer science courses Theory of Computation and Pattern Recognition and Analysis.

Availability

Dr Shakes Chandra is:
Available for supervision

Fields of research

Artificial intelligence Biomedical engineering Biomedical imaging Combinatorics and discrete mathematics (excl. physical combinatorics) Computational imaging Computer vision and multimedia computation Deep learning Engineering Image processing Information and Computing Sciences Machine learning Mathematical Sciences Pure mathematics Theory of computation

Qualifications

  • Doctor of Philosophy, Monash University

Research interests

  • Magnetic Resonance Imaging

    Making MRI faster and more affordable through better image reconstruction, processing and analysis.

  • Image Processing

    Image reconstruction, segmentation and registration.

  • Deep learning

    Dimensionality reduction, machine learning and Artificial Intelligence

  • Fractals and Chaos

    Applying fractals and chaos to image processing and computer science.

  • Number Theory

    Applying number theory to image processing and computer science.

  • Medical Image Analysis

    Medical image segmentation and shape analysis

Research impacts

Times Higher Education - A joint effort: machine learning’s search for the first MRI-based biomarker of osteoarthritis

UQNews - Making magnetic resonance imaging (MRI) faster using fractals and turbulence.

Search Professor Shakes Chandra’s works on UQ eSpace

106 works between 2006 and 2025
All (106) Journal Article (52) Other Outputs (5) Conference Publication (49)

101 - 106 of 106 works

2010

Other Outputs

Circulant theory of the Radon transform

Chandra, Shekhar Suresh (2010). Circulant theory of the Radon transform. PhD Thesis, Faculty of Science, School of Physics, Monash University.

Circulant theory of the Radon transform

2009

Conference Publication

A fast number theoretic finite radon transform

Chandra, S. and Svalbe, I. (2009). A fast number theoretic finite radon transform. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, 1 - 3 December 2009. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2009.67

A fast number theoretic finite radon transform

2008

Conference Publication

An exact, non-iterative Mojette inversion technique utilising ghosts

Chandra, Shekhar, Svalbe, Imants and Guedon, Jean-Pierre (2008). An exact, non-iterative Mojette inversion technique utilising ghosts. 14th International Conference on Discrete Geometry for Computer Imagery, Lyon, France, 16 - 18 April 2008. Heidelberg, Germany: Springer. doi: 10.1007/978-3-540-79126-3_36

An exact, non-iterative Mojette inversion technique utilising ghosts

2008

Conference Publication

A method for removing cyclic artefacts in discrete tomography using latin squares

Chandra, Shekhar and Svalbe, Imants (2008). A method for removing cyclic artefacts in discrete tomography using latin squares. 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Fl United States, 8 - 11 Dec 2008. Washington, DC United States: I E E E Computer Society. doi: 10.1109/ICPR.2008.4761615

A method for removing cyclic artefacts in discrete tomography using latin squares

2006

Journal Article

Quantised angular momentum vectors and projection angle distributions for discrete radon transformations

Svalbe, Imants, Chandra, Shekhar, Kingston, Andrew and Guedon, Jean-Pierre (2006). Quantised angular momentum vectors and projection angle distributions for discrete radon transformations. Discrete Geometry for Computer Imagery, Proceedings, 4245, 134-145.

Quantised angular momentum vectors and projection angle distributions for discrete radon transformations

2006

Conference Publication

Quantised angular momentum vectors and projection angle distributions for discrete radon transformations

Svalbe, Imants, Chandra, Shekhar, Kingston, Andrew and Guédon, Jean-Pierre (2006). Quantised angular momentum vectors and projection angle distributions for discrete radon transformations. 13th International Conference on Discrete Geometry for Computer Imagery, DGCI 2006, , , October 25, 2006-October 27, 2006. Springer Verlag. doi: 10.1007/11907350_12

Quantised angular momentum vectors and projection angle distributions for discrete radon transformations

Current funding

  • 2026 - 2029
    Next generation magnetic resonance imaging through vision
    ARC Future Fellowships
    Open grant
  • 2025 - 2027
    Cost effective and portable low-field musculoskeletal MRI for high performance sport
    Australia's Economic Accelerator Innovate Grants
    Open grant
  • 2020 - 2026
    PREDICT-TBI - PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury: the value of Magnetic Resonance Imaging
    NHMRC MRFF Traumatic Brain Injury Mission
    Open grant

Past funding

  • 2022 - 2025
    Advancing the visualisation and quantification of nephrons with MRI
    ARC Discovery Projects
    Open grant
  • 2022 - 2025
    Robust, valid and interpretable deep learning for quantitative imaging
    ARC Linkage Projects
    Open grant
  • 2021 - 2024
    ChondralHealth Productization: Automated Musculoskeletal MR Image Analysis Algorithms
    Siemens Healthcare Pty Ltd
    Open grant
  • 2021 - 2024
    Osteoarthritis Compass: Personalized prediction of disease onset and progression. (NHMRC Ideas Grant administered by Griffith University)
    Griffith University
    Open grant
  • 2018 - 2022
    MR Hip Intervention and Planning System to enhance clinical and surgical outcomes
    NHMRC Development Grant
    Open grant

Availability

Dr Shakes Chandra is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Next generation magnetic resonance imaging MRI through vision

    Summary: Magnetic resonance imaging (MRI) is crucial for diagnosing diseases within the human body. In this project, we develop new AI methods that leverage human visual perception to make MRI faster and more affordable.

    Technologies such as magnetic resonance imaging (MRI) are essential in healthcare for non-invasively seeing inside the human body for disease diagnosis and assessment. However, imaging cost for MRI is so prohibitive that it is seldom used unless there is no other option despite its effectiveness. The cost is largely because MRI is a slow imaging modality compared to other options that do not provide as much information and soft tissue contrast needed to detect diseases such as cancer. Although some progress has been made to improve acquisition speed, all current methods do not make any allowances for the way that human experts read and understand regions of interest. A reduction in scan time will make MRI cheaper and therefore allow the technology to be more readily utilised in the future.

    This project aims to create new artificial intelligence (AI) models and unify them with MRI acquisition directly in its measurement domain, helping us explain such models and create acquisitions more akin to human vision that only acquires the areas an operator needs, thereby reducing scan times.

Supervision history

Current supervision

Completed supervision

Enquiries

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